Apparatus for predicting life of rotary machine and equipment using the same

ABSTRACT

An apparatus for predicting life expectancy of a rotary machine includes: a load recipe input module acquiring loading conditions of a rotary machine; a characterizing feature input module obtaining characterizing feature data of a rotary machine; and a life expectancy prediction module calculating life expectancy of the rotary machine in conformity with the loading conditions and the characterizing feature data.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is based upon and claims the benefit of priorityfrom prior Japanese Patent Application 2001-085736 filed on Mar. 23,2001; the entire contents of which are incorporated by reference herein.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to a rotary machine life expectancyprediction method, which measures life expectancy of a rotary machine,and a rotary machine repair timing determination method, whichdetermines most appropriate repair timing for a rotary machine based onthe life expectancy thereof.

[0004] 2. Description of the Related Art

[0005] Failure diagnosis has become important for the sake of efficientsemiconductor device manufacturing. Especially as the trend towardslarge item/small volume production of system LSI grows, an efficient yethighly adaptable semiconductor device manufacturing method has becomenecessary. It is possible to use a small-scale production line forefficient production of semiconductor devices. However, if theproduction line is merely reduced, the capacity utilization ofmanufacturing equipments drops. Accordingly, there are problems such asinvestment efficiency falling in comparison with large-scale productionlines. To rectify this situation, there is method where a plurality ofmanufacturing processes is performed on one semiconductor manufacturingequipment. For example, in a low-pressure chemical vapor deposition(LPCVD) system, reactive gases introduced and reaction products differdepending on the types of film depositions. These are evacuated from theLPCVD chamber using a vacuum pump. Accordingly, the film depositionrequirements differ and the formation situations for reaction productswithin the vacuum pump differ depending on the types of manufacturingprocess. Therefore, life expectancy of the vacuum pump is affected bythe process history.

[0006] Normally, sensors for monitoring currents, temperatures, etc.during operation are attached to the vacuum pump. By doing so, whether avacuum pump is malfunctioning can be observed by an operator, either bydirectly viewing them or from information on plotted graphs. However,since the currents and the temperatures change for the vacuum pumpdepending on the various process conditions, it is extremely difficultto measure life expectancy of the vacuum pump from these values, whichchange with every process.

[0007] If the vacuum pump should have an irregular shutdown during filmdeposition in LPCVD, then the lot being processed becomes defective.Moreover, excessive maintenance of the LPCVD system may become necessarydue to microscopic dust caused by residual reactive gases, within thechamber and the piping used for gas introduction or vacuum evacuation.Implementation of such excessive maintenance causes manufacturingefficiency of the semiconductor device to drop dramatically.

[0008] If regular maintenance is scheduled with a margin of safety as ameasure to prevent such sudden irregular shutdowns during themanufacturing process, the frequency for maintaining the vacuum pump maybecome astronomical. Not only does this increase maintenance cost, butit also invites a decrease in capacity utilization of the LPCVD systemdue to changing the vacuum pump, causing the manufacturing efficiency ofthe semiconductor device to drastically decline. To commonly usesemiconductor manufacturing equipment for a plurality of processes,which is required for an efficient small-scale production line, it isdesirable to accurately diagnose vacuum pump life expectancy and tooperate the vacuum pump without having any waste in terms of time.

[0009] Previously, some methods of diagnosing vacuum pump lifeexpectancy have been proposed. In Japanese Patent Application Laid-openNo. 2000-283056, vacuum pump failure forecasting using a plurality ofphysical quantities such as amount of current, temperature or vibrationfor the vacuum pump is disclosed. In addition, it has been disclosedthat operating conditions of the semiconductor manufacturing equipmentsuch as operating time versus stand-by time must be considered toforecast vacuum pump failure. However, it is impossible for this toaccommodate historical results of vacuum pump life expectancy in thecase where a common semiconductor manufacturing equipment is used for aplurality of processes. It is noted that the objective of JapanesePatent Application Laid-open No. 2000-283056 lies in observingabnormalities of a vacuum pump, and not in forecasting life expectancy.Therefore, demands have been made for development of an apparatus andmethod for predicting vacuum pump life expectancy.

SUMMARY OF THE INVENTION

[0010] An apparatus for predicting life expectancy of a rotary machineincludes: a load recipe input module configured to acquire loadingconditions of a rotary machine; a characterizing feature input moduleconfigured to obtain characterizing feature data of a rotary machine;and a life expectancy prediction module calculating life expectancy ofthe rotary machine in conformity with the loading conditions and thecharacterizing feature data.

[0011] A manufacturing equipment using a rotary machine includes: aprocess controller configured to a production process; a rotary machineconfigured to process load of the production process; and a lifeexpectancy prediction controller configured to calculate life expectancyof the rotary machine in conformity with the process recipe obtained bythe process controller and characterizing feature data obtained from therotary machine.

[0012] A method is provided comprising: reading a load recipe of loadingconditions of a rotary machine; determining whether changes exist forthe loading conditions by comparing the load recipe with an alreadyexisting load recipe for the process; employing an already existingdetermination reference if no changes exist for the load conditions, andreading in and employing a determination reference accommodating theprocess conditions if changes exist for the loading conditions insteadof the already existing determination reference; processing time seriesdata by reading in detected characterizing feature data for the rotarymachine, which correspond to the determination reference; andcalculating life expectancy of the rotary machine in conformity with thetime series data and the determination reference.

[0013] A method is provided comprising: reading a load recipe of loadingconditions of a rotary machine; determining whether changes exist forthe loading conditions by comparing the load recipe with an alreadyexisting load recipe for the process; employing an already existingdetermination reference if no changes exist for the loading conditions,and reading in and employing a determination reference accommodating theprocess conditions if changes exist for the loading conditions insteadof the already existing determination reference; processing time seriesdata by reading in detected characterizing feature data for the rotarymachine, which correspond to the determination reference; calculatinglife expectancy of the rotary machine in conformity with the time seriesdata and the determination reference; finding stand-by times of theprocess in a time period until the calculated life expectancy isreached, by a semiconductor production simulator; and determining astand-by time, of the found stand-by times, which least affects theprocess or a time including this stand-by time, to be the replacement orrepair time of rotary machine.

BRIEF DESCRIPTION OF DRAWINGS

[0014]FIG. 1 is a schematic block diagram showing a configuration of asemiconductor manufacturing equipment according the first embodiment ofthe present invention;

[0015]FIG. 2 is a schematic block diagram showing a configuration of ablock of each controller composing an apparatus for predicting lifeexpectancy according the first embodiment of the present invention;

[0016]FIG. 3 is a graph showing an example of time varying current of adry pump during process operation according the first embodiment of thepresent invention;

[0017]FIG. 4 is a graph showing another example of time varying currentof a dry pump during process operation according the first embodiment ofthe present invention;

[0018]FIG. 5 is a flowchart showing a method of predicting dry pump lifeexpectancy according the first embodiment of the present invention;

[0019]FIG. 6 is a block diagram showing a configuration of an apparatusfor predicting life expectancy according a modified example of the firstembodiment of the present invention;

[0020]FIG. 7 is a graph showing an example of time varying current of adry pump until failure according the second embodiment of the presentinvention;

[0021]FIG. 8 is a graph showing auto covariance analysis results for thetime varying current shown in FIG. 6;

[0022]FIG. 9 is a flowchart showing a method of predicting dry pump lifeexpectancy according the third embodiment of the present invention;

[0023]FIG. 10 is a flowchart showing a method of predicting dry pumplife expectancy according the fourth embodiment of the presentinvention;

[0024]FIG. 11 is a block diagram showing a structural example of asemiconductor production system for a method of determining dry pumprepair timing according the fifth embodiment of the present invention;and

[0025]FIG. 12 is a flowchart showing a method of determining dry pumprepair timing according the fifth embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0026] Various embodiments of the present invention will be describedwith reference to the accompanying drawings. It is to be noted that thesame or similar reference numerals are applied to the same or similarparts and components throughout the drawings, and the description of thesame or similar parts and components will be omitted or simplified.

[0027] (First Embodiment)

[0028] As shown in FIG. 1, an LPCVD system used as a semiconductormanufacturing equipment according to the present invention encompasses achamber 1, which has an air-tight structure capable of being evacuated,and is connected on the evacuation side of the chamber 1 to a dry pump5, which is used as a vacuum pump, through a trap 2 and a gate valve 3by a vacuum piping 4. A pump controller 12 controls the operation byoutputting pump control signals 23 in the dry pump 5, and obtains pumpoperation information 24 of the dry pump 5. A plurality of gas pipingare connected to the upstream side of the chamber 1, and this gas pipingis connected to mass flow controllers 7, 8, and 9, respectively. Themass flow controllers 7, 8, and 9 are connected to a gas supply system6, which supplies predetermined gases to be introduced to the chamber 1.A process controller 11 performs control and verification of, forexample, pressures, temperatures and amounts of gas flow inside thechamber 1, in conformity with process control signals 21 and processinformation 22. A life expectancy prediction controller 13 is connectedto the process controller 11 and the pump controller 12. Life expectancyof the dry pump 5 is predicted by reading process conditions for thechamber 1, for example a process recipe 25, which includes processconditions such as types of gases, gas flow rates, pressures, andsubstrate temperatures, from the process controller 11, andcharacterizing feature data 26, which includes, for example,characterizing feature amounts for the dry pump 5 such as currents,temperatures, and vibrations, from the pump controller 12.

[0029] Each controller consists of function blocks as shown in FIG. 2.The process controller 11 has a gas supply control unit 31, a pressurecontrol unit 32, a temperature control unit 33, and the pump controller12 has a pump control unit 35, a current/voltage monitor 36, atemperature monitor 37, a vibration monitor 38, a pressure monitor 39.The life expectancy prediction controller 13 has a process conditionrecipe input module 41, a characterizing feature data input module 42, alife expectancy prediction module 43, an output module 44, and a storageunit 45. The life expectancy prediction module 43 performs calculationof life expectancy of the dry pump 5, by reading a determinationreference corresponding to the process recipe 25 from the storage unit45, and processing statistically the characterizing feature data 26 forthe dry pump 5. The process recipe 25 and the characterizing featuredata 26 are read by the process condition recipe input module 41 and thecharacterizing feature data input module 42, respectively. The outputmodule 44 outputs calculated pump life expectancy information 28 by thelife expectancy prediction module 43 to a server 15. In addition, if ithas become clear that the dry pump 5 is on the verge of failure, theoutput module alerts and sends emergency shutoff signals 29 a and 29 bto the process controller 11 and the pump controller 12, respectively.The storage unit 45 stores process information 22 of the process recipe25, a corresponding life expectancy determination reference, and, also acalculated prediction life expectancy.

[0030] Including such an LPCVD system, various semiconductormanufacturing equipments are integrated as a computer integratedmanufacturing system (CIM) through a local area network (LAN) 14 andadministered in accordance with the CIM-based server (or host computer)15.

[0031] The life expectancy prediction controller 13 can transmit lifeexpectancy results to the CIM-based server 15 as pump life expectancyinformation 28 through the LAN 14. Here, in addition to the dry pumplife expectancy information 28, the server 15 can read in filmdeposition process conditions as lot processing information 27 from theprocess controller 11. In addition, the life expectancy predictioncontroller 13 can send the emergency shutoff signals 29 a, 29 b to theprocess controller 11 and the pump controller 12 immediately beforefailure.

[0032] The life expectancy prediction controller 13, in response to theprocess conditions obtained from the process controller 11 can verifythe operating conditions of the dry pump 5, and therefore is able toperform calculations for predicting life expectancy of the dry pump 5during the process operation. In addition, the amount of precipitatedmaterial accumulated inside the dry pump 5 can be estimated byreferencing the lot processing information 27, which is held in theserver 15, and the life expectancy determination reference value can beupdated through the process history. Moreover, the life expectancyprediction can also be used by constructively utilizing differences inprocess conditions, which are described later.

[0033] Using the LPCVD system shown in FIG. 1, in the case wheredepositing a silicon nitride (Si₃N₄) film, dichlorosilane (SiH₂Cl₂) gasand ammonia (NH₃) gas are respectively introduced via mass flowcontrollers 7 and 8 into the chamber 1 under low pressure conditions ofapproximately several 100 Pa. Inside the chamber 1, a silicon (Si)substrate is heated to approximately 650° C., and through the chemicalreaction of the dichlorosilane and ammonia, a silicon nitride film isdeposited upon the silicon substrate. In addition to generating thesilicon nitride film, this reaction produces reaction by-products ofammonium chloride (NH₄Cl) gas and hydrogen gas. A reaction equation canbe expressed as follows;

3 SiH₂Cl₂+10 NH₃═Si₃N₄+6 NH₄Cl+6 H₂  (1)

[0034] Since hydrogen is a gaseous body, it can be easily purged fromthe dry pump 5. On the other hand, since the temperature of the siliconsubstrate within the chamber 1 is approximately 650° C. and it is underlow pressure of approximately several 100 Pa at the time of formation,the ammonium chloride is also in a gas phase. Generally, the LPCVDsystem has the trap 2 for collecting solid reaction by-product materialdisposed between the chamber 1 and the dry pump 5. With this trap 2, itis impossible to completely collect the by-product material from thereaction under conditions of low pressure. Therefore, the reactionby-product that escapes from the trap 2 without being collected reachesthe dry pump 5. Meanwhile, the not-reacted gas and the by-productmaterial are cooled down. The pressure in the dry pump 5 suddenlyincreases from the low pressure conditions to normal atmosphericpressure due to the compression of the gas. While the by-productammonium chloride is a gaseous body under high temperatures and lowpressure, it solidifies as it cools and the pressure increases. Insidethe dry pump 5, since the evacuated gas is subjected to repeatedcompression, cooled gaseous ammonium chloride throughout the evacuatedgas begins to solidify and precipitate within the dry pump 5. There arecases where the precipitated material adheres and accumulates, and thereare cases where the precipitated material falls off after a certainamount of it has precipitated, which depends upon portions inside thedry pump 5 where the ammonium chloride precipitates. In addition, theprecipitation inside the pump, in particular between the rotor, which isa rotating body, and the casing thereof, causes reduction in clearanceand clogging. In this case, an increase in the amount of a current and apower to the dry pump 5, and an increase in a temperature of the drypump 5, or development of a vibration start to occur instantaneously.However, since smoothing and separation of the precipitated material iscontinuously occurring, the current level or the power level and thetemperature return just as quickly to substantially normal levels andthe vibration decreases. The dry pump 5 repeatedly has the reactionby-product materials precipitated as described above, which ultimatelyleads to its failure.

[0035]FIG. 3 is the time series data of the normal current levels in theearly stage of utilization of the dry pump 5 when the number of timesthe LPCVD has been performed is still low. It can be understood fromthis that precipitation affecting the operation of the dry pump 5 hasnot developed. On the other hand, as the dry pump 5 develops wear andtear, just before failure, humps 51 and spikes 52 indicating abnormalincreases in the current can be seen in the current level time log, asshown in FIG. 4. This shows that the precipitation is frequentlydeveloping over a wide range inside the dry pump 5. Such abnormalincreases in the current begin to occur frequently as the amount ofprecipitated material accumulated inside the dry pump 5 increases.

[0036] The precipitated material continues to increase, and immediatelybefore of the dry pump 5, a temporal increase in the current level orthe power level, the temperature, the vibrations, etc. can be observeddue to the accumulation of precipitated material. For example, theaverage level and the standard deviation of the current in the dry pump5, which are calculated for a certain time period, continue to increasein accordance with the increase in precipitated material. Accordingly,by monitoring this level as a determination reference, life expectancycan be predicted by finding the increasing speed. The life expectancydetermination reference of the dry pump 5 is determined by referencingfailure data. However, as described hereto, in the case where a singlesemiconductor manufacturing equipment is applied to various processes,the determination reference of the life expectancy such as the averagelevel and standard deviation of the current varies with every processcondition. In addition, the increasing speed of characterizing featuredata such as the average level, the standard deviation of the current,etc. depends on the process condition history. Therefore, in the firstembodiment, a respective determination reference level is set for thecharacterizing feature data of every process condition, and lifeexpectancy is calculated through the increasing speed of thecharacterizing feature data during the process. Moreover, in the casewhere the increasing speed of the characterizing feature dataincidentally changes, not only is life expectancy recalculated, but thelife expectancy determination reference level is also updated in linewith this. Thus, it becomes possible for the life expectancy predictionto accommodate various process conditions, and also take the processhistory into consideration.

[0037] The case where the average level and the standard deviation ofthe current in the dry pump 5 are used as the characterizing featuredata 26 for dry pump 5, the life expectancy prediction is describedforthwith with reference to FIGS. 2 and 5. The determination referenceof the dry pump life expectancy and the predicted life expectancy valueare stored for every process condition in the storage unit 45 of thelife expectancy prediction controller 13 as shown in FIG. 2.

[0038] (a) In Step S201 in FIG. 5, the process condition recipe inputmodule 41 in the life expectancy prediction controller 13 reads in theprocess recipe 25 from the process controller 11, and discerns presentprocess conditions of chamber 1, such as types of gases or flow rates ofgases, pressures, and temperatures.

[0039] (b) In Step S202, it is determined whether there is any change inthese present process conditions compared to earlier process conditionsand if it is judged that there are no changes, then the presently setdetermination reference can be used without modification.

[0040] (c) When there has been a change in the process conditions, inStep S203, the life expectancy determination reference value set forevery process condition is renewedly read in from the storage unit 45.

[0041] (d) In Step S204, the characterizing feature data input module 42read in current levels from the characterizing feature data 26 of thedry pump 5.

[0042] (e) In Step S205, the life expectancy prediction module 43calculates the average level and the standard deviation over apredetermined time interval, e.g. 10 seconds, so as to smooth outincidental changes.

[0043] (f) In Step S206, the life expectancy prediction module 43calculates the increasing speed in conformity with the obtained averagelevel and standard deviation of the current, and estimates the length oftime until each determination reference is reached.

[0044] (g) In Step S207, it is determined whether, the predicted lifeexpectancy is normal.

[0045] (h) When the predicted life expectancy is normal, it isdetermined whether the present process is finished, in Step S208. If itis not finished, the procedure is repeated to return in Step S204. If itis finished, then the dry pump 5 becomes in stand-by state until nextprocess (Step S211).

[0046] (i) When it has been discerned, in Step S207, that the predictedlife expectancy is not normal and the dry pump 5 is on the verge offailure, in Step S209, the emergency shutoff signals 29 a, 29 b are sentfrom the output module 44 to the process controller 11 and the pumpcontroller 12. The process controller 11 and the pump controller 12,having received the emergency shutoff signals 29 a, 29 b, executeshutoff sequences of the chamber 1 and the dry pump 5.

[0047] (j) In Step S210, repair or replacement of the dry pump 5 isperformed. Thereafter, the dry pump 5 will be in stand-by state untilnext process (Step S211).

[0048] The life expectancy prediction controller 13 can transfer thepredicted length of time until the determination reference of the drypump 5 is reached, as pump life expectancy information for every processcondition to the server 15 via LAN 14, in Step S206. Based on thetransferred data in the server 15, the pump life expectancy informationfor the dry pump 5 is updated; moreover, if the life expectancydetermination reference is corrected in conformity with changes in theincreasing speed of the characterizing feature data, the updateddetermination reference is returned to the restore unit 45. Naturally,instead of the server 15, storage and processing of this data may beperformed on a separate host computer used as a database upon the LAN14.

[0049] Moreover, when the average level and standard deviation of thecurrent of the dry pump 5 during a process in the chamber 1, increasesbeyond expectation and it has become clear that the dry pump 5 is on theverge of failure, the emergency shutoff signal 29 a is sent from thelife expectancy prediction controller 13 to the process controller 11.The process controller 11, having received the emergency shutoff signal29 a, issues instructions to stop the supply of reactive gases to thechamber 1 and close the gate valve 3, and halts the process. Thisfunction allows the chamber 1 to be protected from contaminationresulting from a sudden shutdown of the dry pump 5.

[0050] According to the first embodiment, since the determinationreference for the characterizing feature data 26 of the dry pump 5 isprescribed for every process condition, the history throughout the lifeexpectancy of the dry pump 5 can be analyzed, and response to changes inthe determination reference according the process condition history ispossible.

[0051] It is noted here that the life expectancy prediction controller13 reads in the process recipe 25 for the type of gas, the flow rate ofgas, etc. from the process controller 11, and discerns the processconditions; however, it is also possible for this reading in to be fromthe server 15 via LAN 14. Alternatively, a host computer used as adatabase can be used in place of the server 15. Here., the average andthe standard deviation of the current level over time are used as thestatistical method for the life expectancy prediction calculation in thelife expectancy prediction controller 13; however, besides this, autocorrelation coefficient according to auto covariance analysis, the lagwidth of the auto correlation coefficient, or likewise can be used. Inaddition, comprehensive determination using not only current levels buta plurality of characterizing feature data is also effective. In thiscase, if a Mahalanobis-Taguchi (MT) distance is used, the prediction canbe made with even greater accuracy. Methods, which apply simpleregression or multiple regression analysis to the increasing speed ofthe characterizing feature data, are effective in raising efficiency ofthe prediction.

[0052] (Modification)

[0053] In the first embodiment, the life expectancy predictioncontroller 13 is shown as an independent apparatus; however, in thealternate example, as shown in FIG. 6, a process/life expectancyprediction control apparatus 18 includes a process controller 11 a and alife expectancy prediction controller 13 a, together. Besides this, itis similar to the first embodiment and the repetitive description isthus abbreviated.

[0054] The process controller 11 a has a gas supply control unit 31 a, apressure control unit 32 a, a temperature control unit 33 a. The lifeexpectancy prediction controller 13 a has a process condition recipeinput module 41 a, a characterizing feature data input module 42 a, alife expectancy prediction module 43 a, an output module 44 a, and astorage unit 45 a. The life expectancy prediction module 43 a performscalculation of life expectancy of the dry pump 5, by reading adetermination reference corresponding to the process recipe in theprocess controller 11 a from the storage unit 45 a, and processingstatistically the characterizing feature data 26 for the dry pump 5. Theprocess recipe and the characterizing feature data 26 are read by theprocess condition recipe input module 41 a and the characterizingfeature data input module 42 a, respectively. The life expectancyprediction results can be transmitted as lot processing information 27 atogether with the process conditions to a server 15 via LAN 14 by anoutput module 44 a. The storage unit 45 a also stores the calculatedlife expectancy data. Process control signals 21 a include an emergencyshutoff signal from the output module 44 a.

[0055] In response to the process conditions, the process/lifeexpectancy prediction control apparatus 18, which is installed both theprocess controller 11 a and the life expectancy prediction controller,can discern the operating conditions of the dry pump 5, and therefore isable to perform calculations for predicting life expectancy of the drypump 5 during operation. In addition, the amount of precipitatedmaterial accumulated inside the dry pump 5, can be estimated byreferencing the lot processing information 27 a, which is held in theserver 15, and the life expectancy determination reference value can beupdated throughout the process history.

[0056] According to the modified example of the first embodiment, sincethe determination reference for the characterizing feature data 26 ofthe dry pump 5 is prescribed for every process condition, the historythroughout the life expectancy of the dry pump 5 can be analyzed, andresponse to changes in the determination reference in conformity withthe process condition history is possible.

[0057] In this modified example, the life expectancy predictioncontroller 13 a is combined with the process controller 11 a; however, asimilar combination may also naturally be possible with the pumpcontroller 12 attached to the dry pump 5.

[0058] (Second Embodiment)

[0059] An example where auto covariance analysis of the dry pump currentis used as a method of predicting life expectancy of a semiconductormanufacturing equipment according the second embodiment of the presentinvention is described forthwith.

[0060] With the semiconductor manufacturing equipment life expectancyprediction method according to the second embodiment of the presentinvention, time series data of characterizing feature data such ascurrents, powers, inner pressures, vibrations, and temperatures obtainedfrom the dry pump are analyzed, and stochastic techniques are used topredict dry pump failure. For example, if a relationship such as “if drypump current is high at a certain point in time, current increases evenafter a predetermined lag width τ (data interval)” can be found, it isuseful in dry pump life expectancy prediction.

[0061] To begin with, in order to analyze time series data of thecharacterizing feature data obtained from the dry pump, an assumption ofconstancy must be made. Simply put, constancy indicates that time seriesdata at each time are realized with the same stochastic process, or thestatistical properties of a stochastic process do not change over time.To have this constancy, conditions must be met where expected valueE[x(t)]=μ remains unchanged over time, expected value E[x(t)2]=μ²remains unchanged over time, or in short the dispersion of x(t) overtime should not change, and further, expected value E[x(t)x(τ)] for anarbitrary t, τ is dependent on only the function of t−τ, or in otherwords expected value E[x(t)x(τ)] is dependent solely on the differencein time. Namely, expected value E[x(t)x(t+τ)] becomes a function of lagwidth τ, and expected value E[x(t)]=μ becomes fixed.

[0062] Therefore, the degree to which variable x(t) and the variablex(t+τ) after lag width τ operate together, or the covariance of x(t) andx(t+τ):

cov(x(t), x(t+τ)=E[(x(t)−μ)(x(t+τ)−μ)]  (2)

[0063] is a function of only lag width (data interval) τ. This isbecause

E[(x(t)−μ)(x(t+τ)−μ)]=E[x(t)x(t+τ)]−μ².  (3)

[0064] This is called auto covariance function C(τ), and is defined as:

C(τ)=E[(x(t)−μ)(x(t+τ)−μ)].  (4)

[0065] Moreover, autocorrelation coefficient ρ_(xx)(τ) is defined as:

ρ_(xx)(τ)=C(τ)/C(0).  (5)

[0066] C(τ) represents the strength of the connection between the dataseparated by lag width τ.

[0067] In other words, when this amount is positively large, variablex(t) and the variable x(t+τ) after lag width τ tend to behave in thesame manner; on the other hand, if it is negatively large, it shows thatvariable x(t) and variable x(t+τ) tend to behave in opposite manners.Also, if this amount is 0, it can be understood that variable x(t) andvariable x(t+τ) behave independent of each other.

[0068] Further by dividing C(τ) by C(o), which is the normal dispersion,the value of ρ_(xx)(τ) can be standardized to be:

−1<ρ_(xx)(τ)≦1.  (6)

[0069] Since the normal dispersion of C (0) represents the strength ofthe relationship with itself, and not a correlation stronger thanitself,

|C(τ)|≦|C(0)|.  (7)

[0070] Eventually, as this auto correlation coefficient ρ_(xx)(τ)approaches 1, it can be determined that there is a strong relationshipbetween variable x(t) and variable x(t+τ), allowing the life expectancyof the semiconductor manufacturing equipment to be predicted. Morespecifically, the time series data for the characterizing feature dataof the initial, non-deteriorated state of the dry pump is measured andmade the reference time series data. The reference auto covariancefunction can be obtained from this reference time series data. Next, thetime series data for the characterizing feature data of the dry pumpduring the process is measured, and from this the auto covariancefunction during the process can be obtained. The auto correlationcoefficient can be found from the process and reference auto covariancefunction. If the auto correlation coefficient is near |1|, it can bedetermined that, regardless of whether the value is positive ornegative, there is a strong relationship between the normalcharacterizing feature data of the dry pump; if it is near 0, then itcan be determined that the correlation is weak and near to the end ofits life expectancy.

[0071] As shown in FIG. 7, from the beginning to the two months periodof the dry pump 5 usage, there are few temporary spikes in the current,but as the usage period progresses over two months until just beforefailure, as described above (refer to FIG. 4), large spikes in thecurrent can be seen. On the other hand, steady changes in the current inthe dry pump 5 are so small that it is almost humanly impossible todetect. Auto covariance analysis carried out based on this data givesthe results shown in FIG. 8. Large, periodic changes in the autocorrelation coefficient become manifest while the dry pump 5 is innormal working order, but as the dry pump 5 wears out as the usageperiod has become longer, these periodic changes become smaller andapproach zero. Accordingly, if these periodic changes are tracked, thecondition of the dry pump 5 can be diagnosed. The life expectancyprediction controller 13 performs diagnosis on the dry pump 5 based onthis signal, and calculates the number of lots that can be processedduring the lifespan of the dry pump 5 and registers this result in theserver 15.

[0072] In the second embodiment, the current level is used as thecharacterizing feature data for the dry pump 5; however, other physicalproperties such as a power level, a temperature, a vibration, or a soundspectrum may be used. In addition, it is also effective to predict thelife expectancy of the dry pump 5 by using not only just the onephysical property of current level, but various physical propertiescomprehensively as the determination reference for the dry pump 5.

[0073] (Third Embodiment)

[0074] Description is made with reference to the example of the LPCVDsystem used in the first embodiment, depicted in FIGS. 2 and 9. In thecase of using not only just the one physical property of current level,but various physical properties comprehensively as the characterizingfeature data for the dry pump 5, life expectancy of the dry pump 5 canbe effectively predicted utilizing a Mahalanobis-Taguchi (MT) distance.

[0075] It is necessary to find an inverse matrix obtained from thereference data during normal conditions, or a reference Mahalanobisspace, in order to find a MT distance with the life expectancyprediction controller 13. For example, the auto correlation coefficientof the auto covariance with respect to the time series data of thecurrent, the temperature, and the vibration of the dry pump 5 may beused as the data forming the reference space. The inverse matrix of thecorrelation matrix derived from the current, the temperature, and thevibration data is then found. Calculation for finding the inverse matrixfrom this correlation matrix can be performed in the life expectancyprediction controller 13; alternatively, it may be performed in theserver 15 or another computer in the CIM system. This referenceMahalanobis space may be set beforehand for every process condition;however, there is also a chance it may change depending on the historyof the various process conditions.

[0076] (a) In Step S501 in FIG. 9, the process condition recipe inputmodule 41 in the life expectancy prediction controller 13 reads in theprocess recipe 25 from the process controller 11, and discerns presentprocess conditions of the chamber 1, such as types of gases or flowrates of gases, pressures, and temperatures.

[0077] (b) In Step S502, it is determined whether there has been achange in the process conditions. When there is no change in the processconditions found, the inverse matrix of the present reference(Mahalanobis) space continues to be used.

[0078] (c) In Step S503 a, when a change in the process conditions hasbeen discerned from the process recipe 25 in Step S502, current,temperature, and vibration data of the dry pump 5 is obtained for apredetermined number of rotations, for example 20 rotations, and with itthe reference data is reconfigured to find a new inverse matrix in StepS503 b.

[0079] (d) Thereafter, in Step S504, the characterizing feature datainput module 42 read in the characterizing feature data 26 of thecurrent levels, the temperatures and the vibrations of the dry pump 5,which are obtained during processing, for a predetermined number ofrotations.

[0080] (e) In Step S505, the life expectancy prediction module 43calculates the inverse matrix from the characterizing feature data 26 ofthe current levels, the temperatures and the vibrations of the dry pump5, which is set as the verified Mahalanobis space. The MT distance isthen found from this verified Mahalanobis space and the reference spacefound earlier, and calculation of the life expectancy of the dry pump 5is performed.

[0081] (f) In Step S506, the life expectancy prediction module 43performs the life expectancy prediction for the dry pump 5. When the drypump 5 is normal, the verified Mahalanobis space is analogous to thereference space and the MT distance shows a value of around 1. A largervalue for the MT distance shows that the verified space and thereference space have deviated, and usually an MT distance ofapproximately 10 is determined to be abnormal. Accordingly, if an MTdistance of 10 is made the life expectancy determination reference forthe dry pump 5, the dry pump life expectancy can be predicted from theMT distance calculated at each measurement point or the speed of theincrease in the MT distance.

[0082] The predicted results are stored in the storage unit 45 by theoutput module 44, and also, registered as the pump life expectancyinformation 28 for each process condition in the server 15 via LAN 14.

[0083] According to the third embodiment, when predicting the lifeexpectancy of the dry pump 5, a correlation matrix of the variousphysical properties is obtained by taking into consideration conditionsof the dry pump 5 and the MT distance can be used to determine lifeexpectancy of the dry pump 5.

[0084] (Fourth Embodiment)

[0085] Since the average value and the standard deviation of thecharacterizing feature data such as currents, powers, temperatures,vibrations, and sounds change correspond to the various processconditions, a method that accommodates this has been described in theabove-mentioned embodiment. In the life expectancy prediction methodaccording to the fourth embodiment, a method, which is simplifiedfurther, is described.

[0086] If the semiconductor manufacturing equipment were to be roughlydivided, it would be said to have two states: the operational statewhere the manufacturing process is being performed and the stand-bystate between when a lot is taken out and the next lot is inserted. Theabove-mentioned first through third embodiments are examples where thelife expectancy prediction of the dry pump 5 is performed duringoperation of the semiconductor manufacturing equipment. During operationof the semiconductor manufacturing equipment, the characterizing featuredata such as the current level is taken during an active process sincethe not-reacted gas and the reaction by-products are being carried fromthe chamber 1 to the dry pump 5. On the other hand, during the stand-bystate, since the chamber 1 is being purged by inactive gas such asnitrogen (N₂) gas, the load on the dry pump 5 is low, and amount ofabnormal material being attached is low, processing is relativelystatic. The fourth embodiment is an example where the life expectancyprediction of the dry pump 5 is performed while the semiconductormanufacturing equipment is on stand-by. In this description, FIGS. 10and 2 are used and the current level of the dry pump 5 is used as thecharacterizing feature data for the life expectancy determinationreference.

[0087] (a) In Step S601, the process condition recipe input module 41 inthe life expectancy prediction controller 13 reads in the process recipe25 from the process controller 11, and discerns the present processconditions of the chamber 1 such as types of gases, flow rates of gases,chamber temperatures, and pressures.

[0088] (b) In Step S602, it is discerned whether the LPCVD system is inan operational state or a stand-by state.

[0089] (c) When it has been discerned that the LPCVD system is in astand-by state, in Step. S604, the characterizing feature data inputmodule 42 read in the current level from the characterizing feature data26 of the dry pump 5.

[0090] (d) In Step S605, the life expectancy prediction module 43calculates the increasing speed in conformity with the average level andthe standard deviation of the current obtained, and estimates the lengthof time until each the life expectancy determination reference read infrom the storage unit 45 is reached.

[0091] (e) In Step S606, the life expectancy prediction module 43predicts the life expectancy from the calculated length of time untilthe determination reference values of the dry pump 5 are reached. Thepredicted results are stored in the storage unit 45 by the output module44, and also, registered as the pump life expectancy information 28 foreach process condition in the server 15 via LAN 14. Based on thetransferred data in the server 15, the pump life expectancy information28 for the dry pump 5 is updated, and moreover, if the life expectancydetermination reference is corrected in conformity with changes in theincreasing speed of the life expectancy, the updated life expectancydetermination reference is returned to the storage unit 45 of the lifeexpectancy prediction controller 13.

[0092] Even if the dry pump 5 is on stand-by, the characterizing featuredata 26 of the dry pump 5 changes for every process due to thehistorical results of the internally precipitated material. Even if onstandby, the level of current in the dry pump 5 increases for everyprocess in conformity with these historical results, and it is possibleto measure the life expectancy of the dry pump 0.5 through analysis ofthe increase speed of the characterizing feature data 26.

[0093] Here, as a stand-by life expectancy prediction for the dry pump5, instead of the constantly flowing purge with nitrogen gas, it is alsoeffective to change the nitrogen gas flow rate. The nitrogen gas flowrate is changed and the amount of change in the average level and thestandard deviation of the current in the dry pump 5 corresponding to thechange in the gas flow rate is measured. When almost at failure, theauthors have found that the change in the average level and the standarddeviation of the current tends to decrease in comparison with the changein the flow rate of nitrogen gas. Accordingly, it is possible to predictthe life expectancy of the dry pump 5 from the change in the averagelevel and the standard deviation of the current compared to the changein the nitrogen gas flow rate. In this manner, it is easier toaccurately determine the life expectancy since life expectancyprediction testing during stand-by can be performed under the conditionsthat are not applicable during operation.

[0094] The life expectancy prediction can be performed by intermittentlyintroducing inactive gas and measuring the change in load features forthe dry pump 5. In this case, the ease with which the attached materialis smoothed and detached, can be studied. As usage of the dry pump 5gets longer, the ability to smooth and detach the attached materialdrops. This change can be picked up as changes in the current level ofthe dry pump 5, allowing the life expectancy prediction to be made forthe dry pump 5.

[0095] In addition, a cleaning gas is sometimes used to remove attachedmaterial within the chamber 1 during stand-by. The change of the currentlevel in this procedure can also be use for prediction. Moreover, evenmore accurate prediction can be attained by intermittently introducingand halting inactive gas after introducing the cleaning gas, andmeasuring the change in the load features of the dry pump 5.

[0096] Effective testing can also be performed by introducing reactivegas, instead of inactive gas. Generally, when introducing inactive gas,changes in the average level and the standard deviation of the loadthereof may become difficult to measure since the load of the dry pump 5is small; in such a case, the use of a reactive gas is effective.

[0097] Unlike during process operation, the same conditions can beemployed to simplify the life expectancy prediction in the case wherethe life expectancy prediction is performed for the dry pump 5 while thesemiconductor manufacturing equipment is on stand-by. In addition, sincethis stand-by is controlled through CIM, the sequence for performinglife expectancy testing can be compiled into an operating program forsemiconductor manufacturing equipment and executed.

[0098] (Fifth Embodiment)

[0099] As shown in FIG. 11, a semiconductor production system comprisesa configuration where a plurality of semiconductor manufacturingequipments 71, 72, . . . are connected to a LAN 14, which is connectedto a server 15, and a semiconductor production simulator 16 is furtherconnected to the server 15.

[0100] Here, assuming a small-scale production line where approximately100 lots are produced per month, there will be somewhere around 50semiconductor manufacturing equipments. The semiconductor productionsimulator 16 configures a virtual factory having the same equipmentconfiguration and scale as this production line using ManSim, which iscommercially available software. Data from the manufacturing equipmentdeployed at the actual production plant such as an equipmentperformance, a processing time, and a frequency of and time required forrepair or quality control (QC) is input to ManSim, and conditions areconstructed inside the computer that are completely identical to thoseof the actual plant. In addition, the semiconductor manufacturingequipments 71, 72, . . . have respective configurations similar to thatof the semiconductor manufacturing equipment shown in FIG. 1.

[0101] Next, a method of determining timing of repairs according to thefifth embodiment of the present invention is described forthwith usingthe flowchart shown in FIG. 12, and the life expectancy predictioncontroller depicted in FIG. 2.

[0102] (a) In Step S801 in FIG. 12, the process condition recipe inputmodule 41 in the life expectancy prediction controller 13 reads in theprocess recipe 25 from the process controller 11, and discerns presentprocess conditions of the chamber 1, such as types of gases, flow ratesof gases, pressures, and temperatures.

[0103] (b) In Step S802, it is determined whether there is any change inthese present process conditions compared to earlier process conditionsand if it is judged that there are no changes, then the presently setdetermination reference can be used without modification.

[0104] (c) When there has been a change in the process conditions, inStep S803, the life expectancy determination reference value set forevery process condition is renewedly read in from the storage unit 45.

[0105] (d) In Step S804, the characterizing feature data input module 42read in current levels from the characterizing feature data 26 of thedry pump 5.

[0106] (e) In Step S805, the life expectancy prediction module 43calculates the average level and the standard deviation over apredetermined time interval, e.g. 10 seconds, so as to smooth outincidental changes.

[0107] (f) In Step S806, the life expectancy prediction module 43calculates the increasing speed in conformity with the obtained averagelevel and the standard deviation of the current, and estimates thelength of time until each determination reference is reached. Thepredicted results are stored in the storage unit 45 by the output module44, and also, registered as the pump life expectancy information 28 foreach process condition in the server 15 via LAN 14.

[0108] (g) In Step S807, it is determined whether the life expectancy ofthe dry pump 5 is predicted drawing near

[0109] (h) When the predicted life expectancy is normal, then the drypump 5 becomes in stand-by state until next process (Step S811), asusual.

[0110] (i) Based on that life expectancy prediction, in Step S807, ifthere is a dry pump for which life expectancy is drawing near, such asthat of the semiconductor manufacturing equipment 71, the server 15causes the semiconductor production simulator 16 to operate and carryout simulation. More specifically, using ManSim, the time period untilfailure is found. Then the repair time period with the least effect onsemiconductor production during that time period is found usingsimulation. Having the least effect on the production means that pumpreplacement is performed when there is no lot coming that should beprocessed into the semiconductor manufacturing equipment 71, or in otherwords, when this equipment is on stand-by. Naturally, as is to beexpected, there are cases where the length of stand-by time is shorterthan required for the pump replacement. In such cases, if the pumpreplacement is performed during the longest of the stand-by times, itwill have the least effect on production.

[0111] (j) It is assumed that it has become clear from the simulationresults, in Step S809, that there are, for example, 1 hour, 3 hour, and5 hour stand-by periods before failure of the dry pump 5 in thesemiconductor manufacturing equipment 71. The time needed to replace thedry pump 5, including letting the temperature of the semiconductormanufacturing equipment 71 drop and bringing the temperature back upafter the replacement, requires approximately 6 hours.

[0112] (k) Therefore, the 5 hour long stand-by period is earmarked, andin Step S810, the dry pump replacement or repair is performed. As aresult, the time that the lot is held up can be limited to only 1 hour.

[0113] According to the fifth embodiment, the semiconductor productionsimulator 16 finds the stand-by time periods for the semiconductormanufacturing equipment through simulation, and determines the stand-byperiod or a time period including this stand-by period with which thesemiconductor production is least affected and sets it as timing of thedry pump repair; accordingly, the length of time the semiconductormanufacture lot is held back can be minimized and the effect on thesemiconductor production is kept to a minimum.

[0114] (Other Embodiments)

[0115] It is noted that the present invention is not limited to theabove-mentioned embodiments, and may also be embodied in various otherforms without departing from the spirit or essential characteristicsthereof, in specific configuration, function, operation or result.Specifically, the life expectancy prediction method is not limited to adry pump or a semiconductor manufacturing equipment, but may have wideapplication to a rotary machine, such as a compressor, a motor, or to amanufacturing equipment using such rotary machine. Furthermore, the drypump life expectancy prediction method is not limited to a dry pump of asemiconductor manufacturing equipment, but may have wide application toan entire semiconductor manufacturing equipment including a dry etchingsystem and a sputtering system. In addition, the vacuum pump is notlimited to being a dry pump, but may also include any variety of pumpssuch as a turbo-molecular pump, a mechanical booster pump, or a rotarypump.

[0116] Various modifications will become possible for those skilled inthe art after receiving the teachings of the present disclosure withoutdeparting from the scope thereof.

[0117] Accordingly, the present invention naturally includes variousembodiments not specifically mentioned herein. Accordingly, thetechnical scope of the present invention may be limited only by theinventive features set forth by the scope of the patent claims deemedreasonable from the above description.

What is claimed is:
 1. An apparatus for predicting life of rotarymachine comprising a load recipe input module configured to acquireloading conditions of a rotary machine; a characterizing feature inputmodule configured to obtain characterizing feature data of said rotarymachine; and a life expectancy prediction module configured to calculatelife expectancy of said rotary machine in conformity with said loadingconditions and said characterizing feature data.
 2. The apparatus ofclaim 1, wherein said loading conditions are defined by processconditions of a chamber where gas is introduced and a semiconductormanufacturing process is performed, and said rotary machine is a vacuumpump.
 3. The apparatus of claim 2, wherein said process conditionsinclude kind of said gas and flow rate of said gas.
 4. The apparatus ofclaim 2, wherein said characterizing feature data include one ofvoltage, current, power, temperature, vibration, and sound of saidvacuum pump.
 5. The apparatus of claim 2, wherein calculation of saidlife expectancy uses time series data of said characterizing featuredata.
 6. The apparatus of claim 5, wherein calculation of said lifeexpectancy uses any one of average value, standard deviation, autocorrelation coefficient, lag width of an auto correlation coefficient,and statistical analysis value of a Mahalanobis-Taguchi distance of alimited time segment in said time series data for characterizing featuredata.
 7. The apparatus of claim 5, wherein said calculation of lifeexpectancy is derived from speed of change of said statistical analysisvalue, a simple regression or multiple regression analysis.
 8. Theapparatus of claim 2, wherein said calculated life expectancy is fed toa local area network.
 9. The apparatus of claim 2, wherein based on saidcalculated life expectancy, an emergency shutoff signal for said processis provided.
 10. Manufacturing equipment comprising: a processcontroller configured to control a production process; a rotary machineconfigured to process load of said production process; and a lifeexpectancy prediction controller configured to calculate life expectancyof said rotary machine in conformity with process recipe obtained bysaid process controller and characterizing feature data obtained fromsaid rotary machine.
 11. The manufacturing equipment of claim 10,wherein said process recipe defines process conditions of a chamberwhere gas is introduced and a semiconductor manufacturing process isperformed, and said rotary machine is a vacuum pump.
 12. Themanufacturing equipment of claim 10, wherein said life expectancyprediction controller is supplied with a plurality of process recipes.13. The manufacturing equipment of claim 11, wherein said processconditions include kind of said gas and flow rate of said gas.
 14. Themanufacturing equipment of claim 0.11, wherein said characterizingfeature data include one of voltage, current, power, temperature,vibration, and sound of said vacuum pump.
 15. The manufacturingequipment of claim 11, wherein calculation of said life expectancy usestime series data of said characterizing feature data.
 16. Themanufacturing equipment of claim 15, wherein said calculation of saidlife expectancy uses any one of average value, standard deviation, autocorrelation coefficient, lag width of an auto correlation coefficient,and statistical analysis value of a Mahalanobis-Taguchi distance of alimited time segment in said time series data for said characterizingfeature data.
 17. The manufacturing equipment of claim 15, wherein saidcalculation of life expectancy is derived from speed of change of saidstatistical analysis value, a simple regression or multiple regressionanalysis.
 18. The manufacturing equipment of claim 11, wherein saidcalculated life expectancy is fed to a local area network.
 19. Themanufacturing equipment of claim 11, wherein based on said calculatedlife expectancy, an emergency shutoff signal for said process isprovided.
 20. The manufacturing equipment of claim 11, wherein saidprocess controller and said life expectancy prediction controller areinstalled in a same case.
 21. A method for predicting life of rotarymachine comprising: reading a load recipe of loading conditions of arotary machine; determining whether changes exist for said loadingconditions by comparing said load recipe with an already existing loadrecipe for the process; employing an already existing determinationreference if no changes exist for said loading conditions, and readingin and employing a determination reference accommodating said processconditions if changes exist for said loading conditions instead of saidalready existing determination reference; processing time series data byreading in detected characterizing feature data for said rotary machine,which correspond to said determination reference; and calculating lifeexpectancy of said rotary machine in conformity with said time seriesdata and said determination reference.
 22. The method of claim 21,wherein said loading conditions are defined by process conditions of achamber where gas is introduced and a semiconductor manufacturingprocess is performed, and said rotary machine is a vacuum pump.
 23. Themethod of claim 22, wherein another semiconductor manufacturing processhaving different object from said semiconductor manufacturing process isfurther performed in said chamber.
 24. A method of claim 22, whereinsaid process conditions include kind of said gas and flow rate of saidgas.
 25. The method of claim 22, wherein said characterizing featuredata include one of voltage, current, power, temperature, vibration, andsound of said vacuum pump.
 26. The method of claim 22, whereincalculation of said life expectancy uses time series data of saidcharacterizing feature data.
 27. The method of claim 26, whereincalculation of said life expectancy uses any one of average value,standard deviation, auto correlation coefficient, lag width of an autocorrelation coefficient, and statistical analysis value of aMahalanobis-Taguchi distance of a limited time segment in said timeseries data for said characterizing feature data.
 28. The method ofclaim 26, wherein said calculation of life expectancy is derived fromspeed of change of said statistical analysis value, a simple regressionor multiple regression analysis.
 29. The method of claim 22, whereinsaid calculated life expectancy is fed to a local area network.
 30. Themethod of claim 22, wherein based on said calculated life expectancy, anemergency shutoff signal for said process is provided.
 31. A method fordetermining timing of rotary machine repair comprising: reading a loadrecipe of loading conditions of a rotary machine; determining whetherchanges exist for said loading conditions by comparing said load recipewith an already existing load recipe for the process; employing analready existing determination reference if no changes exist for saidloading conditions, and reading in and employing a determinationreference accommodating said process conditions if changes exist forsaid loading conditions instead of said already existing determinationreference; processing time series data by reading in detectedcharacterizing feature data for said rotary machine, which correspond tosaid determination reference; calculating life expectancy of said rotarymachine in conformity with said time series data and said determinationreference; finding stand-by times of the process in a time period untilsaid calculated life expectancy is reached, by a semiconductorproduction simulator; and. determining a stand-by time, of said foundstand-by times, which least affects said process or a time includingsaid stand-by time, to be the replacement or repair time of said rotarymachine.
 32. The method of claim 31, wherein said loading conditions aredefined by process conditions of a chamber where gas is introduced and asemiconductor manufacturing process is performed, and said rotarymachine is a vacuum pump.
 33. The method of claim 32, wherein anothersemiconductor manufacturing process having different object from saidsemiconductor manufacturing process is further performed in saidchamber.
 34. The method of claim 32, wherein said process conditionsinclude kind of said gas and flow rate of said gas.
 35. The method ofclaim 32, wherein said characterizing feature data include one ofvoltage, current, power, temperature, vibration, and sound of saidvacuum pump.
 36. The method of claim 32, wherein calculation of saidlife expectancy uses time series data of said characterizing featuredata.
 37. The method of claim 36, wherein calculation of said lifeexpectancy uses any one of average value, standard deviation, autocorrelation coefficient, lag width of an auto correlation coefficient,and statistical analysis value of a Mahalanobis-Taguchi distance of alimited time segment in said time series data for said characterizingfeature data.
 38. The method of claim 36, wherein said calculation oflife expectancy is derived from speed of change of said statisticalanalysis value, a simple regression or multiple regression analysis.